This repository contains a MATLAB implementation of linear support vector machines (SVM) for data points in hyperbolic space. The manuscript describing this method is currently under review. Preprint is available here: https://arxiv.org/abs/1806.00437.
- MATLAB (version R2017b)
- LIBLINEAR for Euclidean support vector classification. Also used as a subroutine in our hyperbolic SVM implementation. Developed with version 2.20.
We provide example scripts for comparing hyperbolic SVM to Euclidean SVM on three benchmark datasets described in our manuscript:
- Gaussian point clouds in hyperbolic space (
run_gaussian.m) - simulated scale-free networks with simulated node labels, embedded in hyperbolic space via LaBNE [1] (
run_randnet.m) - real-world networks with known node labels, embedded in hyperbolic space using the approach of Chamberlain et al. [2] (
run_realnet.m).
Before executing these scripts, edit the following top line in each script
addpath /PATH/TO/LIBLINEAR/matlab % modifyto include the correct path to the MATLAB subdirectory of LIBLINEAR.
Hoon Cho, hhcho@mit.edu
[1] G. Alanis-Lobato, P. Mier, and M.A. Andrade-Navarro. Efficient Embedding of Complex Networks to Hyperbolic Space via their Laplacian. Scientific reports, 6:30108, 2016.
[2] B.P. Chamberlain, J. Clough, and M.P. Deisenroth. Neural Embeddings of Graphs in Hyperbolic Space. arXiv preprint, arXiv:1705.10359, 2017.